Unified model for zero pronoun recovery and resolution

ABSTRACT

A method, computer program, and computer system to recover a dropped pronoun is provided for receiving data corresponding to one or more input words and determining contextual representations for the received input word data. The dropped pronoun may be identified based on a probability value associated with the contextual representations, and a span associated with one or more of the received input words may and that corresponds to which of the input words the dropped pronoun refers may be determined.

CROSS-REFERENCE TO RELATED APPLICATION

This is a continuation of U.S. patent application Ser. No. 16/704,241,filed Dec. 5, 2019, in the United States Patent & Trademark Office, thedisclosure of which is incorporated herein by reference in its entirety.

BACKGROUND

This disclosure relates generally to field of computing, and moreparticularly to natural language processing.

Many languages throughout the world drop subject pronouns fromsentences. While dropping subject pronouns is rare in non-pronoundropping (non-pro-drop) languages such as English, pronoun dropping(pro-drop) occurs frequently in other languages such as Chinese, whereup to 30% of subject pronouns may be dropped. For these languages, thedropped, or zero, pronoun may correspond to a sentence subject that mayeasily be deduced from context. In these cases, the subject may bedropped for simplicity and efficiency without introducing ambiguity forhuman listeners.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forrecovering dropped pronouns. According to one aspect, a method forrecovering dropped pronouns is provided. The method may includereceiving, by a computer, data corresponding to one or more input wordsand determining contextual representations for the received input worddata. The computer may identify the dropped pronoun based on aprobability value associated with the contextual representations and maydetermine a span associated with one or more of the received input wordscorresponding to which of the input words the dropped pronoun refers.

According to another aspect, a computer system for recovering droppedpronouns is provided. The computer system may include one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include receiving, by a computer,data corresponding to one or more input words and determining contextualrepresentations for the received input word data. The computer mayidentify the dropped pronoun based on a probability value associatedwith the contextual representations and may determine a span associatedwith one or more of the received input words corresponding to which ofthe input words the dropped pronoun refers.

According to yet another aspect, a computer readable medium forrecovering dropped pronouns is provided. The computer readable mediummay include one or more computer-readable storage devices and programinstructions stored on at least one of the one or more tangible storagedevices, the program instructions executable by a processor. The programinstructions are executable by a processor for performing a method thatmay accordingly include receiving, by a computer, data corresponding toone or more input words and determining contextual representations forthe received input word data. The computer may identify the droppedpronoun based on a probability value associated with the contextualrepresentations and may determine a span associated with one or more ofthe received input words corresponding to which of the input words thedropped pronoun refers.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating one skilled in the art in understandingthis disclosure in conjunction with the detailed description. In thedrawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a block diagram of a program that detects and recovers droppedpronouns, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program that detects and recovers dropped pronouns, according to atleast one embodiment;

FIG. 4 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment;

FIG. 5 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 4, according to at least one embodiment;and

FIG. 6 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Aspects of this disclosure may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this disclosure to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

Embodiments relate generally to the field of computing, and moreparticularly to natural language processing. The following describedexemplary embodiments provide a system, method and program product to,among other things, predict whether a pronoun was dropped from asentence and recover the dropped pronoun based on context in thesentence. Therefore, some embodiments have the capacity to improve thefield of computing by allowing for computers to recover dropped pronounsand determine the predicates associated with the dropped subjectpronouns. Thus, the computer-implemented method, computer system, andcomputer readable medium disclosed herein may, among other things, beused to improve natural language processing applications for users whospeak a pronoun-dropping language.

As previously described, many languages throughout the world dropsubject pronouns. While dropping subject pronouns is rare in non-pronoundropping (non-pro-drop) languages such as English, pronoun dropping(pro-drop) occurs frequently in other languages such as Chinese, whereup to 30% of subject pronouns can be dropped. For these languages, thedropped, or zero, pronoun may correspond to a sentence subject that mayeasily be deduced from context. In these cases, the subject may bedropped for simplicity and efficiency without introducing ambiguity forhuman listeners. However, pronoun dropping may cause issues forcomputers utilizing natural language processing (NLP) as they may beunable to determine the dropped subject pronouns. Zero pronoun droppingmay cause a loss of information, such as the subject or the object ofthe central predicate in a sentence, which may introduce ambiguity tomany NLP applications, such as machine translation, question-answeringbased on multi-turn conversation, and dialogue understanding.

There are two long-existing tasks for zero pronouns: zero pronounrecovery, which may be used to recover the original pronoun, and zeropronoun resolution, which may be used to determine the words in thesentence to which each dropped pronoun may refer. Because the results ofthe two tasks highly interact with each other, it may be advantageous,therefore, to solve both tasks together. The present method, computersystem, and computer-readable medium may jointly solve both tasks undera multi-task learning framework. For example, a Bidirectional EncoderRepresentation from Transformers (BERT) model may be used. The model maybe trained in a heterogeneous way on multiple mixed datasets before themodel may be updated, based on the task-specific loss value of a batchat each training step.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to certain embodiments. It will be understoodthat each block of the flowchart illustrations and/or block diagrams,and combinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer readable program instructions.

The following described exemplary embodiments provide a system, methodand program product that detects and recovers dropped pronouns.According to the present embodiment, this detection and classificationmay be provided through contextual analysis of received input words anda calculation of probabilities associated with the detection, recovery,and resolution of dropped pronouns.

Referring now to FIG. 1, a functional block diagram of a networkedcomputer environment illustrating a zero pronoun recovery system 100(hereinafter “system”) for improved detecting, recovery, and resolutionof dropped pronouns is shown. It should be appreciated that FIG. 1provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 6 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 4 and 5. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for detecting, recovering,and resolving dropped pronouns is enabled to run an Zero PronounRecovery Program 116 (hereinafter “program”) that may interact with adatabase 112. The Zero Pronoun Recovery Program method is explained inmore detail below with respect to FIG. 3. In one embodiment, thecomputer 102 may operate as an input device including a user interfacewhile the program 116 may run primarily on server computer 114. In analternative embodiment, the program 116 may run primarily on one or morecomputers 102 while the server computer 114 may be used for processingand storage of data used by the program 116. It should be noted that theprogram 116 may be a standalone program or may be integrated into alarger zero pronoun recovery program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring to FIG. 2, a block diagram of a Zero Pronoun Recovery Program116 is depicted. FIG. 2 may be described with the aid of the exemplaryembodiments depicted in FIG. 1. According to one or more embodiments,the Zero Pronoun Recovery Program 116 may be located on the computer 102(FIG. 1) or on the server computer 114 (FIG. 1). The Zero PronounRecovery Program 116 may accordingly include, among other things, aBidirectional Encoder Representation from Transformers (BERT) Encoder202 and a Zero Pronoun (ZP) Function Module 204. The BERT Encoder 202may receive one or more input words 206A-206N and may append a [CLS]class token to the input words 206A-206N. The BERT encoder 202 mayoutput contextual representations 208A-208N that may correspond to eachof the input words 206A-206N. The BERT Encoder 202 may also output acontextual representation 208′ that may correspond to a contextualrepresentation for the [CLS] token. The ZP Function Module 204 mayreceive the contextual representations 208′ and 208A-208N as inputs toperform one or more functions.

The task of zero pronoun recovery may be to restore any dropped pronounsfor an input text. Since pronouns are enumerable, the Zero PronounRecovery Program 116 may cast this task into a classification problemfor each word space. The ZP Function Module 204 may be configured toperform a zero pronoun recovery function ƒ_(rec) using the contextualrepresentations 208A-208N output by the BERT encoder 202. Theprobability for recovering a pronoun rec_(i) at a space between inputword 206(i−1) and input word 206 i may be p(rec_(i)|X,i)=softmax(W_(rec)[h_(i-1), h_(i)]+b_(rec)), where W_(rec) and b_(rec)may be parameters for a linear classifier and h may correspond to thecontextual representations 208′ and 208A-208N.

The Zero Pronoun Recovery Program 116 may perform a zero pronounresolution function ƒ_(res) by predicting a span associated with eachdropped pronoun. According to one embodiment, zero pronoun recovery maybe performed first and the information may be utilized in zero pronounresolution. According to an alternative embodiment, a span of “(0,0)”may be assigned for non-zero-pronoun positions (i.e., a position havingan input word). This may minimize conflicts because the position “0” maycorrespond to the [CLS] token, such that no real spans can be “(0,0).”The Zero Pronoun Recovery Program 116 may cast the zero pronounresolution task for each word space between input word 206(i−1) andinput word 206 i as a machine reading-comprehension problem, where theresolution span may correspond to a target answer. Start and endpositions may be separately determined for each span based onconcatenation of all word states and self-attention modules forpredicting the start and end positions of each zero pronoun resolutionspan. Thus, the ZP Function Module 204 may be configured to perform azero pronoun resolution function ƒ_(res). The probability of predictingthe whole span res_(i) may be p(res_(i)|X, i)=SelfAttn_(st)(H,i)SelfAttn_(ed)(H, i)=p(res_(i) ^(st)|X, i)p(res_(i) ^(ed)|X, i), whereH may be the concatenation of the contextual representations 208′ and208A-208N and SelfAttn_(st) and SelfAttn_(ed) may be the self-attentionmodules for the start and end positions, respectively.

To improve the robustness of heterogeneous training, zero pronoundetection may be utilized as both a sub-task for zero pronoun detectionand zero pronoun recovery, as well as an additional training value.Because zero pronoun detection may be a binary task that determinedwhether each word space has a zero pronoun, zero pronoun recovery may beavailable across multiple types of datasets. As an auxiliary task, theZero Pronoun Recovery Program 116 may detect dropped pronouns forimproved training. Since determining whether a sentence may have adropped pronoun may be a binary classification, the ZP Function Module204 may be configured to perform a zero pronoun detection functionƒ_(det). The probability of detecting a dropped pronoun may bep(det_(i)|X, i)=softmax(W_(det)[h_(i-1), h_(i)]+b_(det)), where W_(det)and b_(det) may be model parameters and h may correspond to thecontextual representations 208′ and 208A-208N.

The ZP Function Module 204 may be trained using combined and shuffleddata of both zero pronoun recovery and zero pronoun resolution tasks inorder to leverage more supervision values. For example, each datainstance may only contain an annotation of either zero pronoun recoveryor zero pronoun resolution. Thus, a loss value for one example may bedefined as loss=−Σ_(i∈1 . . . N) α log p(rec_(i)|X, i)−β logp(res_(i)|X, i)−γ log p(det_(i)|X, i), where α, β and γ may serve as acoefficient and indicator for each task. For α and β, the value may be“1” if a corresponding supervision value exists; otherwise it may be“0.” Because a supervision value associated with zero pronoun detectionmay exist for all instances, the value of γ may be set to a low value inorder to avoid skewing the loss function.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program that recovers dropped pronouns isdepicted. FIG. 3 may be described with the aid of FIGS. 1 and 2. Aspreviously described, the Zero Pronoun Recovery Program 116 (FIG. 1) mayquickly and effectively detect and recover dropped pronouns.

At 302, data corresponding to one or more input words is received by acomputer. The input words may be, for example, a sentence insubstantially any language. The sentence may have, among other things, adropped subject pronoun. In operation, the Zero Pronoun Recovery Program116 (FIG. 1) may be stored on the computer 102 (FIG. 1) or the servercomputer 114 (FIG. 1). According to one embodiment, the Zero PronounRecovery Program 116 may retrieve the input words 206A-206N (FIG. 2)from the database 112 (FIG. 1) on the server computer 114. According toan alternative embodiment, the input words 206A-206N may be stored onthe data storage device 106 (FIG. 1) on the computer 102 and the ZeroPronoun Recovery Program 116 may receive the input words 206A-206N fromthe computer 102 over the communication network 110 (FIG. 1).

At 304, contextual representations are determined for the received inputword data by the computer. The contextual representations may bedetermined by a Bidirectional Encoder Representation from Transformers(BERT) model. The contextual representations may be used to determinehow the one or more input words are used in the context of theircorresponding sentence. In operation, the BERT Encoder 202 (FIG. 2) mayreceive the input words 206A-206N as input and may output contextualrepresentations 208A-208N (FIG. 2) that may correspond to the inputwords 206A-206N. The BERT encoder 202 may append a [CLS] class token tothe start of the input words 206A-206N and may output a contextualrepresentation 208′ corresponding to the [CLS] token.

At 306, the dropped pronoun is identified by the computer, based on aprobability value associated with the contextual representations.Because pronouns are enumerable and there are a limited number ofpossible pronouns and corresponding types, the recovery of droppedpronouns may be cast into a classification problem for each word space.Thus, the computer may be able to determine, based on the context, whichpronoun was dropped from the sentence. In operation, the ZP FunctionModule 204 (FIG. 2) may perform the zero pronoun recovery functionƒ_(rec) on the contextual representations 208′ and 208A-208N (FIG. 2).The ZP Function Module 204 may determine, based on probabilityp(rec_(i)|X, i) which pronoun was dropped from the sentence containingthe input words 206A-206N (FIG. 2).

At 308, a span associated with one or more of the received input wordsis determined by the computer. The span may correspond to one or more ofthe input words to which the dropped pronoun refers. By determiningwhich words make up the predicate corresponding to the dropped subjectpronoun, the determination of the recovered subject pronoun may beimproved. In operation, the ZP Function Module 204 (FIG. 2) may performthe zero pronoun resolution function ƒ_(res) on the contextualrepresentations 208′ and 208A-208N (FIG. 2) in order to predict the spanof the predicate to which the dropped pronoun refers. The ZP FunctionModule 204 may determine the span, based on probability p(res_(i)|X, i).

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 4, illustrative cloud computing environment 400 isdepicted. As shown, cloud computing environment 400 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 400 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 400 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 5, a set of functional abstraction layers 500 providedby cloud computing environment 400 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of thedisclosure are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Zero Pronoun Recovery 96. Zero PronounRecovery 96 may detect recover dropped pronouns for sentences written inlanguages that may allow the use of zero pronouns.

FIG. 6 is a block diagram 600 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 6 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Zero Pronoun Recovery Program 116 (FIG. 1) on server computer114 (FIG. 1) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMs 822 (which typically include cache memory). In the embodimentillustrated in FIG. 6, each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Zero Pronoun Recovery Program 116 (FIG. 1)can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theZero Pronoun Recovery Program 116 (FIG. 1) on the server computer 114(FIG. 1) can be downloaded to the computer 102 (FIG. 1) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Zero PronounRecovery Program 116 on the server computer 114 are loaded into therespective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method comprising: receiving, by a computer,data indicating words; determining, by the computer, contextualrepresentations, for the words indicated by the data, by a BidirectionalEncoder Representation from Transformers (BERT) encoder; andidentifying, by the computer, a dropped pronoun based on a probabilityvalue associated with the contextual representations and determined bysoftmax(W_(rec)[h_(i-1), h_(i)]+b_(rec)), wherein i−1 corresponds to afirst word, i corresponds to a second word, h corresponds to thecontextual representations, and W_(rec) and b_(rec) correspond toparameters for a linear classifier.
 2. The method of claim 1, furthercomprising detecting, by the computer, that the dropped pronoun wasdropped.
 3. The method of claim 2, wherein the dropped pronoun isdetected using a binary classification based on a second probabilityvalue associated with the contextual representations.
 4. The method ofclaim 2, further comprising calculating a loss value based on thedetecting of the dropped pronoun, the identifying of the droppedpronoun, and the determining of the span.
 5. The method of claim 1,further comprising appending, by the computer, a class token to thedata, wherein the appended class token corresponds to a start positionassociated with the words.
 6. The method of claim 5, wherein a span ofzero is assigned to the appended class token.
 7. The method of claim 1,further comprising: determining, by the computer, that the datacorresponding to the dropped pronoun; and determining, by the computer,a type associated with the dropped pronoun.
 8. The method of claim 1,wherein the probability value is a probability of recovering the droppedpronoun at a space between the first input word and the second inputword.
 9. The method of claim 1, further comprising: determining, by thecomputer, a span associated with one or more of the words correspondingto which of the words the dropped pronoun refers, wherein a start and anend position for the span is determined based on a concatenation of thewords and on a self-attention module for predicting the start positionand the end position of the span.
 10. The method of claim 1, wherein theidentifying the dropped pronoun based on the probability valueassociated with the contextual representations comprises identifying,based on the probability value, that the dropped pronoun would occurbetween two of the words.
 11. A computer system comprising: one or morecomputer-readable non-transitory storage media configured to storecomputer program code; and one or more computer processors configured toaccess said computer program code and operate as instructed by saidcomputer program code, said computer program code including: receivingcode configured to cause the one or more computer processors to receivedata indicating words; determining code configured to cause the one ormore computer processors to determine contextual representations, forthe words indicated by the data, by a Bidirectional EncoderRepresentation from Transformers (BERT) encoder; and identifying codeconfigured to cause the one or more computer processors to identify adropped pronoun based on a probability value associated with thecontextual representations and determined by softmax(W_(rec)[h_(i-1),h_(i)]+b_(rec)), wherein i−1 corresponds to a first input word, icorresponds to a second input word, h corresponds to the contextualrepresentations, and W_(rec) and b_(rec) correspond to parameters for alinear classifier.
 12. The system of claim 11, further comprisingdetecting code configured to cause the one or more computer processorsto detect that the dropped pronoun was dropped.
 13. The system of claim12, wherein the dropped pronoun is detected using a binaryclassification based on a second probability value associated with thecontextual representations.
 14. The system of claim 12, furthercomprising calculating code configured to cause the one or more computerprocessors to calculate a loss value based on the detecting of thedropped pronoun, the identifying of the dropped pronoun, and thedetermining of the span.
 15. The system of claim 11, further comprisingappending code configured to cause the one or more computer processorsto append a class token to the data, wherein the appended class tokencorresponds to a start position associated with the words.
 16. Thesystem of claim 11, further comprising: determining code configured tocause the one or more computer processors to determine that the datacorresponds to the dropped pronoun; and determining code configured tocause the one or more computer processors to determine a type associatedwith the dropped pronoun.
 17. The system of claim 11, wherein theprobability value is a probability of recovering the dropped pronoun ata space between the first input word and the second input word.
 18. Thesystem of claim 11, further comprising: determining code configured tocause the one or more computer processors to determine a span associatedwith the one or more of the words corresponding to which of the wordsthe dropped pronoun refers, wherein a start and an end position for thespan is determined based on a concatenation of the words and on aself-attention module for predicting the start position and the endposition of the span.
 19. The system of claim 11, wherein theidentifying code is configured to cause the one or more computerprocessors to identify, based on the probability value, that the droppedpronoun would occur between two of the words.
 20. A non-transitorycomputer readable medium having stored thereon a computer programconfigured to cause one or more computer processors to: receive dataindicating words; determine contextual representations, for the one ormore words indicated by the data, by a Bidirectional EncoderRepresentation from Transformers (BERT) encoder; identify a droppedpronoun based on a probability value associated with the contextualrepresentations and determined by softmax(W_(rec)[h_(i-1),h_(i)]+b_(rec)), wherein i−1 corresponds to a first input word, icorresponds to a second input word, h corresponds to the contextualrepresentations, and W_(rec) and b_(rec) correspond to parameters for alinear classifier.